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import os
import streamlit as st
from token_stream_handler import StreamHandler
from chat_profile import User, Assistant, ChatProfileRoleEnum
from langchain.chains import ConversationalRetrievalChain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import Docx2txtLoader, PyPDFLoader, TextLoader
from langchain_community.vectorstores.chroma import Chroma
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
st.set_page_config(page_title="InkChatGPT", page_icon="π")
def load_and_process_file(file_data):
"""
Load and process the uploaded file.
Returns a vector store containing the embedded chunks of the file.
"""
file_name = os.path.join("./", file_data.name)
with open(file_name, "wb") as f:
f.write(file_data.getvalue())
_, extension = os.path.splitext(file_name)
# Load the file using the appropriate loader
if extension == ".pdf":
loader = PyPDFLoader(file_name)
elif extension == ".docx":
loader = Docx2txtLoader(file_name)
elif extension == ".txt":
loader = TextLoader(file_name)
else:
st.error("This document format is not supported!")
return None
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
chunks = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings(openai_api_key=st.session_state.api_key)
vector_store = Chroma.from_documents(chunks, embeddings)
return vector_store
def initialize_chat_model(vector_store):
"""
Initialize the chat model with the given vector store.
Returns a ConversationalRetrievalChain instance.
"""
llm = ChatOpenAI(
model="gpt-3.5-turbo",
temperature=0,
openai_api_key=st.session_state.api_key,
)
retriever = vector_store.as_retriever()
return ConversationalRetrievalChain.from_llm(llm, retriever)
def main():
"""
The main function that runs the Streamlit app.
"""
assistant_message = "Hello, you can upload a document and chat with me to ask questions related to its content."
st.session_state["messages"] = [
Assistant(message=assistant_message).build_message()
]
st.chat_message(ChatProfileRoleEnum.Assistant).write(assistant_message)
if prompt := st.chat_input(
placeholder="Chat with your document",
disabled=(not st.session_state.api_key),
):
st.session_state.messages.append(User(message=prompt).build_message())
st.chat_message(ChatProfileRoleEnum.User).write(prompt)
handle_question(prompt)
def handle_question(question):
"""
Handles the user's question by generating a response and updating the chat history.
"""
crc = st.session_state.crc
if "history" not in st.session_state:
st.session_state["history"] = []
response = crc.run(
{
"question": question,
"chat_history": st.session_state["history"],
}
)
st.session_state["history"].append((question, response))
for msg in st.session_state.messages:
st.chat_message(msg.role).write(msg.content)
with st.chat_message(ChatProfileRoleEnum.Assistant):
stream_handler = StreamHandler(st.empty())
llm = ChatOpenAI(
openai_api_key=st.session_state.api_key,
streaming=True,
callbacks=[stream_handler],
)
response = llm.invoke(st.session_state.messages)
st.session_state.messages.append(
Assistant(message=response.content).build_message()
)
def display_chat_history():
"""
Displays the chat history in the Streamlit app.
"""
if "history" in st.session_state:
st.markdown("## Chat History")
for q, a in st.session_state["history"]:
st.markdown(f"**Question:** {q}")
st.write(a)
st.write("---")
def clear_history():
"""
Clear the chat history stored in the session state.
"""
if "history" in st.session_state:
del st.session_state["history"]
def build_sidebar():
with st.sidebar:
st.title("π InkChatGPT")
with st.form(key="input_form"):
openai_api_key = st.text_input(
"OpenAI API Key",
type="password",
placeholder="Enter your OpenAI API key",
)
st.session_state.api_key = openai_api_key
if not openai_api_key:
st.info("Please add your OpenAI API key to continue.")
uploaded_file = st.file_uploader(
"Select a file", type=["pdf", "docx", "txt"], key="file_uploader"
)
add_file = st.form_submit_button("Process File")
if add_file and uploaded_file and openai_api_key.startswith("sk-"):
with st.spinner("π Thinking..."):
vector_store = load_and_process_file(uploaded_file)
if vector_store:
crc = initialize_chat_model(vector_store)
st.session_state.crc = crc
st.success(
f"File: `{uploaded_file.name}`, processed successfully!"
)
if __name__ == "__main__":
build_sidebar()
main()
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